{"id":"W2037873670","doi":"10.1016/j.ultrasmedbio.2004.07.009","title":"On the potential of the Lagrangian speckle model estimator to characterize atherosclerotic plaques in endovascular elastography: In vitro experiments using an excised human carotid artery","year":2005,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Speckle pattern; Elastography; Biomedical engineering; Carotid arteries; Scanner; Ultrasound; Radiology; Computer science; Medicine; Artificial intelligence; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007364794,0.0003431418,0.0007218138,0.0007122272,0.0001076682,0.00001179548,0.0004504941,0.0001448228,0.00007860863],"category_scores_gemma":[0.0004032878,0.0002087975,0.0001826766,0.0007366405,0.0005748255,0.00009701164,0.00004690259,0.0005932449,0.000003582099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239667,"about_ca_system_score_gemma":0.0000602918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009304196,"about_ca_topic_score_gemma":0.000241148,"domain_scores_codex":[0.9975321,0.0003453305,0.0007242731,0.0005126835,0.0003040728,0.0005815459],"domain_scores_gemma":[0.9985524,0.0002753932,0.0001557684,0.0008281781,0.00004880753,0.00013944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002489561,0.0005180652,0.05389003,0.00001699484,0.00004067595,0.000002683079,0.002673445,0.001202035,0.9410402,0.0000955707,0.00001942742,0.0002519845],"study_design_scores_gemma":[0.01393532,0.002586542,0.8894719,0.002122782,0.000251888,0.0002614655,0.00430958,0.007701804,0.0752134,0.002665852,0.0005115422,0.000967967],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966427,0.0001381762,0.0008531224,0.001026991,0.0001979254,0.0009197897,0.00001754849,0.00002984969,0.000173907],"genre_scores_gemma":[0.9952118,0.00003361354,0.002685472,0.001698175,0.0002029075,0.00006536442,0.00005358211,0.00003910415,0.000009937825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8658267,"threshold_uncertainty_score":0.8514514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870834353940391,"score_gpt":0.2908175986079712,"score_spread":0.2621092550685672,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}